Conventionally, transmission of image information and storage of the image information in a storage device require a steep communications charge and a large capacity storage. To efficiently transmit or store image information, various technologies are currently used for compressing images.
One such example is to reduce an amount of coding by using image features and to omit a portion of information. Another is an entropy coding which assigns a short code to a value which frequently occurs. Another is to reduce an amount of coding by increasing a statistical bias.
An image compression method includes an information conversion process and an entropy coding process to represent the image information with minimum codes, 1 and 0. In the information conversion process, as mentioned previously, a previous processing for matching image contents is performed by reducing the amount of coding, omitting a portion of information, modifying images to features rather than pixels, and converting image features so as to increase a statistical bias. In the entropy coding, a short code is assigned to a value which frequently occurs.
Additionally, sound compression also possesses problems in that sound is susceptible to noise and distortion during transmission. This causes these inaccuracy information to be stored in a recording medium. To efficiently transmit or store sound information, various technologies are currently used for compressing sound. One such example is to covert analog signals into digital signals to reduce noise, distortion, and degradation of information during transmission. However, bit rates may well be limited to 9.6 k bits/second based on current modem technology. Since the above-described techniques take 60 k bits/second to transmit the signals, the resultant signals cannot be transmitted via a communication line. Therefore, the sound signal compression (i.e., low bit coding) is required to transmit information.
As distribution of a signal amplitude is uniform, a given bit is effectively used and much information can be transmitted. According to this theory, the distribution of the signal amplitude is made as uniform as possible before it is coded. As a result, much information can be transmitted with less bits.
From this theory, a non-linear (logarithmic) companding PCM and a differential PCM and the like are further improved to develop an ADPCM (Adaptive Differential PCM).
In an image compression apparatus, a discrete cosine transform (DCT) is performed on an 8 times 8 pixel block to quantize the resultant value. In many cases, an alternating, or AC component are Huffman coded by scanning in a zigzag towards a high frequency from a low frequency (i.e., a zigzag scan). The Huffman codes are of variable length so that the codes are variable based on a likelihood of occurrence.
In addition, there is a trade off between a code amount and an original image. In general, increasing the size of a quantization table for less codes significantly degrades image quality. On the other hand, decreasing the size of the quantization table for less image degradation usually increases the code amount.
FIG. 1 is a drawing illustrating an example of a quantized 8 times 8 block register in a DCT coefficient algorithm. Labels r00, r01, r02, . . . r76, r77 are applied in FIG. 1 to reference elements of the 8 times 8 block register in FIG. 1. Such labels are not shown in, but also apply to corresponding elements in, the 8 times 8 block registers of FIGS. 5-7.
Zigzag arrows show a sequence of zigzag scanning at the time of Huffman coding. In the example of FIG. 1, pixel r00 represents a directing, or DC component which shows intensities (i.e., brightness) of 8 times 8 blocks. The other components are AC components represented by a slope from a low-frequency component to a high-frequency component as indicated by a translucent arrow.
After quantization, the coefficient is not likely to exist in the high frequency component. If any, the coefficient has little influence on images.
However, the Huffman coding is run length-dependent, but it is not affected by frequencies. If the high-frequency component has a isolated coefficient, an amount of coding is increased.
Japanese Laid-Open Patent Application Publication, No. 2000-125295, entitled “MOVING PICTURE CODER, ITS METHOD, MOVING PICTURE DECODER, ITS METHOD AND STORAGE MEDIUM” describes a method for reducing the quantity of transmission data while optimizing Huffman codes.
According to the above-mentioned specification, an optimum Huffman code is designed from quantized DCT transform coefficient data. An existing Huffman code is compared with the newly designed Huffman code to discriminate the necessity of a change of the Huffman code. When the new Huffman code is selected, a Huffman code table of the new Huffman code is stored in a table buffer, the new Huffman code table is added to the head of a JPEG file, and Huffman coding is applied to quantized DCT transform coefficient data and the result is added to the JPEG file. when the Huffman coding is applied to the data by using the existing Huffman code table, the Huffman code table does not need to be added to each frame.
Thus, the existing Huffman code table includes redundant information, which causes a decrease in coding efficiency. The new Huffman code table is optimally designed depending on a statistical nature of each frame. In the method of the above-mentioned specification, the difference between the existing Huffman code and the newly designed Huffman code is determined. If the resultant difference is less than a predetermined threshold value, the existing Huffman code table is utilized. If the difference is greater than the predetermined threshold value, the newly designed Huffman code table is utilized.
As described previously, in the image compression apparatus, there is a trade off between a code amount and an original image. In general, increasing the size of a quantization table for less codes significantly degrades image quality. On the other hand, decreasing the size of the quantization table for less image degradation usually increases the code amount.
To reduce the code amount while maintaining fidelity with an original image, an optimum quantized table needs to be selected from image types to be coded, as described in the above-mentioned specification. All existing techniques, however, possess their own distinct disadvantages. In a digital copier and a printer application, the optimum quantized table is downloaded for each image, which causes a decrease in system performance from a viewpoint of a processing speed.
Moreover, when the image types are not predicable, the quantized table is often predetermined in accordance with degree of image compression. As a result, an output code is not always the optimum code.
For sound compression, the same problems as the image compression occurs. There is a trade off between a code amount and an sound amount. Since the distribution of the signal amplitude is made as uniform as possible before it is coded, much information can be transmitted with less bits. However, when too few bits are transmitted, fidelity of sound is decreased.